meta %>%
filter(su_blkgp == 1) %>%
select(description) %>% as.list()
## $description
## [1] "# of low-wage jobs in the su (earnings $1250/month or less )"
## [2] "# of mid-wage jobs in the su (earnings $1251/month to $3333/month)"
## [3] "# of high-wage jobs in the su (earnings greater than $3333/month)"
## [4] "Total # of jobs in the su"
## [5] "Total # of workers who live in the su"
## [6] "Total # of workers who live in the su divided by the total # of jobs in the su"
## [7] "# of low-wage jobs in the su divided by the total # of jobs in the su"
## [8] "# of mid-wage jobs in the su divided by the total # of jobs in the su"
## [9] "# of high-wage jobs in the su divided by the total # of jobs in the su"
## [10] "# of White alone workers employed in the su"
## [11] "# of Black alone workers employed in the su"
## [12] "# of American Indian or Alaska Native alone workers employed in the su"
## [13] "# of Asian alone workers employed in the su"
## [14] "# of Native Hawaiian or Other Pacific Islander alone workers employed in the su"
## [15] "# of workers employed in the su who identify as two or more race groups"
## [16] "# of jobs for workers with less than a high school education"
## [17] "# of jobs for workers with a high school educaiton but no college"
## [18] "# of jobs for workers with some college or an Associates degree"
## [19] "# of jobs for workers with a Bachelor's or advanced degree"
## [20] "5-digit county code"
## [21] "12-digit census block group code"
## [22] "County name"
glimpse(lodes)
## Rows: 155
## Columns: 20
## $ w_blkgroup <dbl> 510030101001, 510030101002, 510030101003, 510…
## $ lowwage_jobs <int> 44, 81, 49, 48, 118, 33, 37, 205, 14, 440, 20…
## $ midwage_jobs <int> 58, 83, 63, 73, 177, 40, 96, 157, 52, 783, 49…
## $ higwage_jobs <int> 41, 55, 60, 258, 181, 39, 108, 90, 63, 1782, …
## $ alljobs <int> 143, 219, 172, 379, 476, 112, 241, 452, 129, …
## $ lowwage_p <dbl> 0.3076923, 0.3698630, 0.2848837, 0.1266491, 0…
## $ midwage_p <dbl> 0.4055944, 0.3789954, 0.3662791, 0.1926121, 0…
## $ higwage_p <dbl> 0.2867133, 0.2511416, 0.3488372, 0.6807388, 0…
## $ White_workers <int> 128, 204, 149, 296, 316, 96, 207, 371, 108, 2…
## $ Black_workers <int> 12, 11, 13, 55, 115, 10, 26, 64, 19, 354, 12,…
## $ AI_Na_workers <int> 1, 0, 0, 1, 0, 0, 2, 1, 0, 6, 0, 0, 0, 0, 2, …
## $ Asian_workers <int> 0, 2, 9, 20, 31, 2, 2, 14, 1, 148, 3, 10, 3, …
## $ NaH_PI_workers <int> 0, 0, 1, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ Multiracial_workers <int> 2, 2, 0, 6, 12, 2, 4, 2, 1, 46, 3, 0, 2, 4, 7…
## $ lessThanHS_jobs <int> 20, 25, 17, 34, 42, 17, 25, 38, 16, 217, 11, …
## $ HSnoCollege_jobs <int> 36, 46, 42, 97, 109, 19, 56, 73, 26, 574, 24,…
## $ SomeColl_Associates_jobs <int> 32, 60, 42, 89, 115, 27, 73, 98, 32, 688, 31,…
## $ Bach_AdvDeg_jobs <int> 14, 45, 36, 98, 82, 29, 42, 93, 30, 825, 23, …
## $ w_county <int> 51003, 51003, 51003, 51003, 51003, 51003, 510…
## $ countyName <chr> "Albemarle", "Albemarle", "Albemarle", "Albem…
lodes %>% select(lowwage_jobs:Bach_AdvDeg_jobs) %>%
select(where(~is.numeric(.x))) %>%
as.data.frame() %>%
stargazer(., type = "text", title = "Summary Statistics", digits = 2,
summary.stat = c("mean", "sd", "min", "median", "max"))
##
## Summary Statistics
## ===========================================================
## Statistic Mean St. Dev. Min Median Max
## -----------------------------------------------------------
## lowwage_jobs 175.44 313.91 1 72 2,352
## midwage_jobs 238.54 439.40 0 98 3,086
## higwage_jobs 346.51 1,034.46 0 83 9,734
## alljobs 760.49 1,714.50 10 284 14,612
## lowwage_p 0.29 0.13 0.06 0.29 0.82
## midwage_p 0.37 0.10 0.00 0.38 0.70
## higwage_p 0.34 0.16 0.00 0.31 0.83
## White_workers 598.00 1,346.47 1 223 11,566
## Black_workers 118.08 245.58 0 38 1,615
## AI_Na_workers 2.28 4.59 0 1 37
## Asian_workers 28.63 105.72 0 6 1,146
## NaH_PI_workers 0.52 1.41 0 0 11
## Multiracial_workers 12.98 30.20 0 4 237
## lessThanHS_jobs 63.95 116.09 0 30 878
## HSnoCollege_jobs 150.65 287.01 1 64 2,235
## SomeColl_Associates_jobs 174.17 391.33 0 65 3,072
## Bach_AdvDeg_jobs 197.86 592.62 0 56 5,950
## -----------------------------------------------------------
loch_missingness_monster(lodes)
## There are 0 missing values in the dataset
## The maximum number of values that any variable is missing is 0
##
## Number of missing values per variable:
## w_blkgroup lowwage_jobs midwage_jobs higwage_jobs alljobs lowwage_p midwage_p
## 0 0 0 0 0 0 0
## higwage_p White_workers Black_workers AI_Na_workers Asian_workers
## 0 0 0 0 0
## NaH_PI_workers Multiracial_workers lessThanHS_jobs HSnoCollege_jobs
## 0 0 0 0
## SomeColl_Associates_jobs Bach_AdvDeg_jobs w_county countyName
## 0 0 0 0
lodes %>% select(c(w_blkgroup:alljobs)) %>%
pivot_longer(-w_blkgroup, names_to = "measure", values_to = "value") %>%
ggplot(aes(x = value, fill = measure)) +
scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
geom_histogram() +
facet_wrap(~measure, scales = "free")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## [1] TRUE
pal <- colorNumeric("plasma", reverse = TRUE, domain = cvl_lodes$alljobs)
leaflet(cvl_lodes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cvl_lodes,
fillColor = ~pal(alljobs),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 1, fillOpacity = 0.8, bringToFront = T
),
popup = paste0("Block Group: ", cvl_lodes$BLKGRPCE, "<br>",
"Number of jobs: ", cvl_lodes$alljobs, 2)) %>%
addLegend("bottomright", pal = pal, values = cvl_lodes$alljobs,
title = "Number of jobs", opacity = 0.7)
pal <- colorNumeric("plasma", reverse = TRUE, domain = cvl_lodes$lowwage_p)
leaflet(cvl_lodes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cvl_lodes,
fillColor = ~pal(lowwage_p),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
highlight = highlightOptions(
weight = 1, fillOpacity = 0.8, bringToFront = T
),
popup = paste0("Block Group: ", cvl_lodes$BLKGRPCE, "<br>",
"Prop. low-wage jobs: ", round(cvl_lodes$lowwage_p, 2))) %>%
addLegend("bottomright", pal = pal, values = cvl_lodes$lowwage_p,
title = "Proportion of low wage jobs", opacity = 0.7)
# High wage jobs
pal <- colorNumeric("plasma", reverse = TRUE, domain = cvl_lodes$higwage_p)
leaflet(cvl_lodes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cvl_lodes,
fillColor = ~pal(higwage_p),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
smoothFactor = 0.3,
highlight = highlightOptions(
weight = 1, fillOpacity = 0.8, bringToFront = T
),
popup = paste0("Block Group: ", cvl_lodes$BLKGRPCE, "<br>",
"Prop. high-wage jobs: ", round(cvl_lodes$higwage_p, 2))) %>%
addLegend("bottomright", pal = pal, values = cvl_lodes$higwage_p,
title = "Proportion of high-wage jobs", opacity = 0.7)
pal <- colorNumeric("plasma", reverse = TRUE, domain = cvl_lodes$Bach_AdvDeg_jobs)
leaflet(cvl_lodes) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(data = cvl_lodes,
fillColor = ~pal(Bach_AdvDeg_jobs),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.6,
smoothFactor = 0.3,
highlight = highlightOptions(
weight = 1, fillOpacity = 0.8, bringToFront = T
),
popup = paste0("Block Group: ", cvl_lodes$BLKGRPCE, "<br>",
"Number of jobs: ", cvl_lodes$Bach_AdvDeg_jobs)) %>%
addLegend("bottomright", pal = pal, values = cvl_lodes$Bach_AdvDeg_jobs,
title = "Number of jobs for college-educated workers", opacity = 0.7)